AI predicts precursors to heart attacks

According to the Center for Disease Control and Prevention, over 610,000 people die of heart disease every year, which is the leading cause of death for both men and women in the U.S. Fortunately, scientists at IBM and pharmaceutical giant AstraZeneca are investigating a machine learning framework that can suss out early ASC warning signs. It's described in a newly published paper ("Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention") on the preprint server Arxiv.org. The team sourced a dataset containing the age, gender, personal disease history, habits, laboratory test results, procedures, ACS type, and nearly 40 other characteristics of 26,986 adult hospitalized patients across 38 urban and rural hospitals in China, which they fed to a neural network -- i.e., layers of mathematical functions loosely modeled after biological neurons. Said neural network was architected to predict four factors simultaneously: whether they'd experienced a major adverse cardiac event, or MACE, prior to ACS; whether they'd received antiplatelet medicine to prevent blood clots from forming in the coronary arteries; whether they'd been given beta-blockers, which reduce blood pressure; and whether they were prescribed statins, a class of drugs that help lower cholesterol levels (and in turn prevent heart attacks and stroke). The paper's authors next employed k-means clustering -- a statistical technique in which data points are allocated to collections by similarities -- to organize the patients into seven groups based on the classification data obtained from the neural network.